Asymptotically Zero Energy Computing Using Split-Level Charge Recovery Logic

The dynamic power requirement of CMOS circuits is rapidly becoming a major concern in the design of personal information systems and large computers. In this work we present a number of new CMOS logic families, Charge Recovery Logic (CRL) as well as the much improved Split-Level Charge Recovery Logic (SCRL), within which the transfer of charge between the nodes occurs quasistatically. Operating quasistatically, these logic families have an energy dissipation that drops linearly with operating frequency, i.e., their power consumption drops quadratically with operating frequency as opposed to the linear drop of conventional CMOS. The circuit techniques in these new families rely on constructing an explicitly reversible pipelined logic gate, where the information necessary to recover the energy used to compute a value is provided by computing its logical inverse. Information necessary to uncompute the inverse is available from the subsequent inverse logic stage. We demonstrate the low energy operation of SCRL by presenting the results from the testing of the first fully quasistatic 8 x 8 multiplier chip (SCRL-1) employing SCRL circuit techniques.

Sequential Optimal Recovery: A Paradigm for Active Learning

In most classical frameworks for learning from examples, it is assumed that examples are randomly drawn and presented to the learner. In this paper, we consider the possibility of a more active learner who is allowed to choose his/her own examples. Our investigations are carried out in a function approximation setting. In particular, using arguments from optimal recovery (Micchelli and Rivlin, 1976), we develop an adaptive sampling strategy (equivalent to adaptive approximation) for arbitrary approximation schemes. We provide a general formulation of the problem and show how it can be regarded as sequential optimal recovery. We demonstrate the application of this general formulation to two special cases of functions on the real line 1) monotonically increasing functions and 2) functions with bounded derivative. An extensive investigation of the sample complexity of approximating these functions is conducted yielding both theoretical and empirical results on test functions. Our theoretical results (stated insPAC-style), along with the simulations demonstrate the superiority of our active scheme over both passive learning as well as classical optimal recovery. The analysis of active function approximation is conducted in a worst-case setting, in contrast with other Bayesian paradigms obtained from optimal design (Mackay, 1992).

Proactive Detection and Recovery of Lost Mobile Phones

This paper describes the successful implementation of a prototype software application that independently and proactively detects whether a mobile phone is lost or misused. When the mobile phone is detected as being lost or misused, the application takes steps to mitigate the impact of loss and to gather evidence. The goal is to aid in the recovery of the mobile phone. The prototype works regardless of the cellular infrastructure the mobile phone is operating in and makes minimum demands on the owner of the mobile phone. The prototype was developed on Nokia 6600 mobile phones that run Symbian Operating System 7.0s. Development was done using Nokia’s Series 60 Developer’s Platform 2.0.

Personnel Practices in Japanese Government

This paper examines key aspects of Japanese Government personnel practices, and draws comparisons with the practices in the U.S. civil service and, as appropriate, with other U.S. models. Particular reference is made to the practices of two major agencies in Japan, the Ministry of International Trade and Industry (MITI) and the Ministry of Posts and Telecommunications (MPT). As appropriate, selected government businesses practices other than personnel practices are noted.